Time series anomaly detection is an important and challenging problem, and it has been studied over decades across wide application domains, including intelligence transportation [1]}, health care [2]}, KPI monitoring [3]}, web intrusion detection [4]}, environmental monitoring [5]}, and fault diagnosis [6]}, malware detection [7]} etc. Such a broad application is also reflected in the diversity of formulations and models to time series anomaly detection. In contrast to anomaly detection on static data, one widely accepted idea is that the temporal continuity plays a key role in all formulations, and unusual changes in the time series data are detected and used to model anomalies. For multi-dimensional correlated time series data, the correlations make the problem further complex, which highlights the need for robust and flexible models.
